Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition

By Yao Wang, Jiangjun Peng, Qian Zhao, Yee Leung, Xile Zhao, Deyu Meng. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017

453 citations recorded by the source corpus.

Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, etc. Such complex noise could degrade the quality of the acquired HSIs, limiting the precision of the subsequent processing. In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part. Specifically, for the clean HSI part, we use tensor Tucker decomposition to describe the global correlation among all bands, and an anisotropic spatial–spectral total variation regularization to characterize the piecewise smooth structure in both spatial and spectral domains. For the mixed noise part, we adopt the $\ell _1$ norm regularization to detect the sparse noise, including stripes, impulse noise, and dead pixels. Despite that TV regularization has the ability of removing Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian noise for some real-world scenarios. Then, we develop an efficient algorithm for solving the resulting optimization problem by using the augmented Lagrange multiplier method. Finally, extensive experiments on simulated and real-world noisy HSIs are carried out to demonstrate the superiority of the proposed method over the existing state-of-the-art ones.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c375a76cdfeab208b57eb711584f478c99cdfb29

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Citations in the corpus

Hyperspectral Image Denoising via Tensor Low-Rank Prior and Unsupervised Deep Spatial–Spectral Prior (2022, methodology): “Considering the piecewise smooth structure of HSIs, Wang et al. [31] proposed a TV regularized low-rank tensor decomposition model for HSI denoising, in which Tucker decomposition is utilized to capture the global structure, and TV is utilized to capture the piecewise smooth structure.”
SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers (2021, background): “, denoising, missing data recovery) [2]–[4], dimensionality reduction [5]– [7], spectral unmixing [8]–[10], and feature extraction [11]– [13].”